const hits, misses = 1023, 1

	benchMap(b, bench{
		setup: func(_ *testing.B, m mapInterface) {
			for i := 0; i < hits; i++ {
				m.LoadOrStore(i, i)
			}
			// Prime the map to get it into a steady state.
			for i := 0; i < hits*2; i++ {
				m.Load(i % hits)
			}
		},

		perG: func(b *testing.B, pb *testing.PB, i int, m mapInterface) {
			for ; pb.Next(); i++ {
				m.Load(i % (hits + misses))
			}

 *
 * <p>To use this class the concrete subclass must implement the {@link
 * IteratorTester#newTargetIterator()} method. This is because it's impossible to test an Iterator
 * without changing its state, so the tester needs a steady supply of fresh Iterators.
 *
 * <p>If your iterator supports modification through {@code remove()}, you may wish to override the
 * verify() method, which is called after each sequence and is guaranteed to be called

func TestGcPacer(t *testing.T) {
	t.Parallel()

	const initialHeapBytes = 256 << 10
	for _, e := range []*gcExecTest{
		{
			// The most basic test case: a steady-state heap.
			// Growth to an O(MiB) heap, then constant heap size, alloc/scan rates.
			name:          "Steady",
			gcPercent:     100,
			memoryLimit:   math.MaxInt64,
			globalsBytes:  32 << 10,
			nCores:        8,
			allocRate:     constant(33.0),

 *
 * <p>To use this class the concrete subclass must implement the {@link
 * IteratorTester#newTargetIterator()} method. This is because it's impossible to test an Iterator
 * without changing its state, so the tester needs a steady supply of fresh Iterators.
 *
 * <p>If your iterator supports modification through {@code remove()}, you may wish to override the
 * verify() method, which is called after each sequence and is guaranteed to be called

func updateReplicaSetStatus(logger klog.Logger, c appsclient.ReplicaSetInterface, rs *apps.ReplicaSet, newStatus apps.ReplicaSetStatus) (*apps.ReplicaSet, error) {
	// This is the steady state. It happens when the ReplicaSet doesn't have any expectations, since
	// we do a periodic relist every 30s. If the generations differ but the replicas are
	// the same, a caller might've resized to the same replica count.

		}
	}

	// We have 3 major flows to handle:
	// 1. kubelet running, normal allocation (needed > 0, container being  [re]created). Steady state and most common case by far and large.
	// 2. kubelet restart. In this scenario every other component of the stack (device plugins, app container, runtime) is still running.

#### Endpoints

Endpoints have an optimized code path, as they are by far the most frequently updated resource - in a steady cluster, this will often be the *only* change, caused by scale up/down.

		// is based on a steady-state scannable heap size, we assume this means our
		// heap is growing. Compute a new heap goal that takes our existing runway
		// computed for scanWorkExpected and extrapolates it to maxScanWork, the worst-case
		// scan work. This keeps our assist ratio stable if the heap continues to grow.
		//
		// The effect of this mechanism is that assists stay flat in the face of heap

		// (so it would be made more efficient by promoting the dirty
		// map to read-only).
		// Count it as a miss so that we will eventually switch to the
		// more efficient steady state.
		m.missLocked()
	}
	return swapped
}

// CompareAndDelete deletes the entry for key if its value is equal to old.
// The old value must be of a comparable type.
//

          "showLineNumbers": false,
          "showMiniMap": false
        },